Detecting Early-Stage Alzheimer’s Disease: A Structured Review of Current Methodologies and Future Directions

Abstract

Alzheimer’s disease (AD) is a progressive, incurable neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. Due to its gradual onset and the difficulty of distinguishing pathological changes from normal aging, early diagnosis of AD remains a significant clinical challenge. Detecting AD at an early stage is crucial, as it enables timely intervention and improved disease management. In recent years, computational approaches have played an increasingly important role in early-stage AD detection. This paper presents a comprehensive review of existing methodologies for early-stage AD detection, including traditional machine learning models, deep learning architectures and emerging generative artificial intelligence techniques such as Generative Adversarial Networks (GANs) and diffusion models. The study analyzes commonly used datasets, imaging modalities and evaluation metrics used in recent research. The strengths and limitations of various approaches are discussed and a comparative overview is provided to highlight current research trends.